A review of machine learning applications in wildfire science and management (2003.00646v2)

Abstract: Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of ML in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.

Machine Learning in Wildfire Science: A Comprehensive Overview

The reviewed paper provides an extensive examination of the application of ML in wildfire science and management, highlighting its evolution since the early 1990s and focusing on its proliferation in recent years. The paper identifies a significant number of published works—300 papers as identified by the authors—emphasizing the versatility and adaptability of ML methods across various dimensions of wildfire research.

Key Applications and Methodologies

The paper categorizes the use of ML in wildfire science into six primary domains: fuels characterization, fire detection and mapping, fire weather and climate change, fire occurrence and risk, fire behavior prediction, and fire management. For each domain, it delineates the specific applications and challenges addressed by ML methods.

1. Fuels Characterization and Fire Detection: Early research utilized neural networks to assess vegetation characteristics and moisture content, crucial for predicting fire behavior. The detection of wildfires has seen the employment of more advanced methods, particularly Convolutional Neural Networks (CNNs), which excel in processing spatial imagery from terrestrial and satellite sources for detecting fire and smoke.
2. Fire Perimeter and Severity Mapping: This domain has benefitted from advancements in remote sensing and ML. Algorithms like Support Vector Machines (SVM), Random Forests (RF), and CNNs have been effectively used to map burn areas and assess fire severity—key components for post-fire evaluation and management.
3. Fire Weather and Climate Change: ML applications here involve predicting fire-conducive weather conditions and assessing fire risk under future climate change scenarios. Techniques such as random forests and Bayesian networks support the identification of critical weather patterns and climate factors influencing fire events.
4. Fire Occurrence and Risk: ML methods, notably MaxEnt, RF, and Boosted Regression Trees (BRT), are employed to predict fire occurrences and map susceptibility. These models leverage environmental, climatic, and anthropogenic predictors to estimate spatial risk, facilitating better fire readiness and prevention strategies.
5. Fire Behavior Prediction: Predicting fire spread and behavior is essential for managing active fires and developing control strategies. Methods including Reinforcement Learning and Genetic Algorithms have been explored, allowing for optimization of simulation models and the assimilation of real-time data during fire events.
6. Fire Management: Relatively underexplored compared to other domains, ML use in fire management shows potential in optimizing resource allocation and policy implementation. It involves learning from historical data to improve decision-making processes, enabling better strategic planning in fire-prone areas.

Implications and Future Directions

The paper underscores the burgeoning role of ML in wildfire science but also highlights notable gaps, especially in fire management and prescriptive analytics. While many applications focus on prediction and classification, there is a pressing need to develop ML models that support operational decision-making and resource optimization in real-time fire scenarios.

Furthermore, the paper emphasizes the importance of high-quality, accessible data. Collaboration between ML researchers and wildfire experts is crucial for refining models to incorporate domain-specific knowledge, ensuring realistic modeling of complex fire processes. This collaboration can also facilitate the transition of research innovations into practical, operational tools for fire management agencies.

Conclusion

In summarizing the diverse applications and future potential of ML in wildfire science, this paper serves as an informative guide for researchers and practitioners. The breadth of methodologies, from traditional ensemble methods to cutting-edge neural networks, illustrates ML's capacity to tackle wildfire-related challenges. As ML techniques continue to evolve, they promise to enhance our understanding and management of wildfires, particularly under the pressures of climate change and increasing fire incidence.